Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition Article Swipe
Rongbing Huang
,
Chang Liu
,
Guoqi Li
,
Jiliu Zhou
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1155/2016/6795352
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1155/2016/6795352
Based on a special type of denoising autoencoder (DAE) and image reconstruction, we present a novel supervised deep learning framework for face recognition (FR). Unlike existing deep autoencoder which is unsupervised face recognition method, the proposed method takes class label information from training samples into account in the deep learning procedure and can automatically discover the underlying nonlinear manifold structures. Specifically, we define an Adaptive Deep Supervised Network Template (ADSNT) with the supervised autoencoder which is trained to extract characteristic features from corrupted/clean facial images and reconstruct the corresponding similar facial images. The reconstruction is realized by a so-called “bottleneck” neural network that learns to map face images into a low-dimensional vector and reconstruct the respective corresponding face images from the mapping vectors. Having trained the ADSNT, a new face image can then be recognized by comparing its reconstruction image with individual gallery images, respectively. Extensive experiments on three databases including AR, PubFig, and Extended Yale B demonstrate that the proposed method can significantly improve the accuracy of face recognition under enormous illumination, pose change, and a fraction of occlusion.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2016/6795352
- http://downloads.hindawi.com/journals/mpe/2016/6795352.pdf
- OA Status
- hybrid
- Cited By
- 19
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2566228241
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2566228241Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2016/6795352Digital Object Identifier
- Title
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Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face RecognitionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2016Year of publication
- Publication date
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2016-01-01Full publication date if available
- Authors
-
Rongbing Huang, Chang Liu, Guoqi Li, Jiliu ZhouList of authors in order
- Landing page
-
https://doi.org/10.1155/2016/6795352Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/mpe/2016/6795352.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/mpe/2016/6795352.pdfDirect OA link when available
- Concepts
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Artificial intelligence, Autoencoder, Pattern recognition (psychology), Computer science, Face (sociological concept), Deep learning, Facial recognition system, Computer vision, Image (mathematics), Artificial neural network, Iterative reconstruction, Social science, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2020: 4, 2019: 5, 2018: 4Per-year citation counts (last 5 years)
- References (count)
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12Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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